CN113009463A - Human body detection method and device - Google Patents
Human body detection method and device Download PDFInfo
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- CN113009463A CN113009463A CN202110134941.4A CN202110134941A CN113009463A CN 113009463 A CN113009463 A CN 113009463A CN 202110134941 A CN202110134941 A CN 202110134941A CN 113009463 A CN113009463 A CN 113009463A
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Abstract
The application discloses a human body detection method and a human body detection device, which comprise the following steps: acquiring an intermediate frequency signal, wherein the intermediate frequency signal is formed by mixing a transmitting signal of a millimeter wave radar and a received echo signal; detecting the intermediate frequency signal by using a region detection algorithm and judging whether a target enters a designated region, and detecting the intermediate frequency signal by using a multi-time micro-motion detection algorithm to judge whether a person exists in the designated region; if no, returning to the step of acquiring the intermediate frequency signal. By the aid of the design scheme, the two detection algorithms, namely the region detection algorithm and the micro-motion detection algorithm, can be fused on a result level, and the micro-motion detection algorithm is used for judging whether a human body exists in the designated region for multiple times, so that errors of results can be reduced, and accuracy of human body detection results is improved.
Description
Technical Field
The application relates to the technical field of millimeter wave radar detection, in particular to a human body detection method and device.
Background
At present, in the field of smart home, passive infrared sensors, 5.8G radars, network cameras and the like are mostly used for detecting whether human bodies exist. The passive infrared sensor only can detect dynamic human bodies but cannot detect static human bodies by using human body infrared radiation as a trigger source, and is easily influenced by factors such as environment, temperature and the like to generate a false alarm phenomenon; the 5.8G radar mainly uses the Doppler effect to detect dynamic targets, but cannot distinguish human bodies from non-human bodies. Therefore, it is necessary to provide a human body detection method to solve the above problems.
Disclosure of Invention
The technical problem mainly solved by the application is to provide a human body detection method and device, which can improve the accuracy of a human body detection result.
In order to solve the technical problem, the application adopts a technical scheme that: provided is a human body detection method including: acquiring an intermediate frequency signal, wherein the intermediate frequency signal is formed by mixing a transmitting signal of a millimeter wave radar and a received echo signal; detecting the intermediate frequency signal by using a region detection algorithm and judging whether a target enters a designated region, and detecting the intermediate frequency signal by using a multi-time micro-motion detection algorithm to judge whether a person exists in the designated region; if no, returning to the step of acquiring the intermediate frequency signal.
After the step of detecting the intermediate frequency signal by using the region detection algorithm and judging that the target enters the designated region, the method further comprises the following steps: and setting the area detection zone bit of the designated area as a first zone bit.
Responding to the detection of the intermediate frequency signal by using a region detection algorithm and judging that a target leaves a designated region, setting a region detection zone bit of the designated region as a second zone bit, and returning to the step of acquiring the intermediate frequency signal; wherein the first flag bit is different from the second flag bit.
Wherein, still include: responding to the detection of the intermediate frequency signal by using a region detection algorithm and judging whether no target enters or leaves the designated region, and obtaining a current region detection zone bit of the designated region; responding to the current region detection zone bit as a first zone bit, and detecting the intermediate frequency signal by utilizing a multi-time inching detection algorithm to judge whether a person exists in the specified region; and responding to the second zone bit of the current zone detection zone bit, and returning to the step of acquiring the intermediate frequency signal.
Wherein the step of detecting the intermediate frequency signal in response to the use of the region detection algorithm and determining that a target enters the designated region comprises: responding to the detection of the intermediate frequency signal by using a region detection algorithm and judging that a dynamic target exists in the intermediate frequency signal, and obtaining the distance between the dynamic target and the millimeter wave radar; judging whether the distance is smaller than or equal to a first threshold value; and if so, judging that the dynamic target enters the specified area.
The step of detecting the intermediate frequency signal by using a multi-time micro-motion detection algorithm to judge whether a person exists in the designated area comprises the following steps of: obtaining distance information of the dynamic target, and analyzing phase sequence information of a distance peak value in the distance information; performing band-pass filtering processing on the phase sequence information to extract a frequency spectrum in a target respiratory frequency range; obtaining a power spectral density within a plurality of the target respiratory frequency ranges from the frequency spectrum; obtaining a mean value of a plurality of the power spectral densities, and judging whether the mean value is larger than a second threshold value; and if so, judging that the specified area is occupied.
The step of obtaining the distance information of the dynamic target and analyzing the phase sequence information of the distance peak in the distance information includes: and obtaining the distance information of the dynamic target by using a one-dimensional fast Fourier transform formula, and analyzing the phase sequence information of a distance peak value in the distance information.
Before the step of detecting the intermediate frequency signal by using the region detection algorithm and judging that a target enters the designated region, the method further comprises the following steps: and eliminating radar echo of a static target in the intermediate frequency signal by using a dynamic target detection filter.
In order to solve the above technical problem, another technical solution adopted by the present application is: there is provided a human body detection apparatus comprising a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the human body detection method mentioned in any of the above embodiments.
In order to solve the above technical problem, the present application adopts another technical solution: there is provided an apparatus having a storage function, which stores program data capable of being read by a computer, the program data being executable by a processor to implement the human body detection method mentioned in any of the above embodiments.
Different from the prior art, the beneficial effects of the application are that: the method comprises the steps of obtaining an intermediate frequency signal, wherein the intermediate frequency signal is formed by mixing a transmitting signal of a millimeter wave radar and a received echo signal, responding to the fact that an area detection algorithm is used for detecting the intermediate frequency signal and judging that a target enters a specified area, detecting the intermediate frequency signal by using a multi-time micro-motion detection algorithm to judge whether a person exists in the specified area, and if no person exists, returning to the step of obtaining the intermediate frequency signal. By the method, the two detection algorithms, namely the region detection algorithm and the micro-motion detection algorithm, can be fused on a result level, and whether a human body exists in the designated region is judged by utilizing the multi-time micro-motion detection algorithm, so that the error of the result can be reduced, and the accuracy of the human body detection result is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a human body detection method according to the present application;
FIG. 2 is a schematic flow chart illustrating an embodiment of step S2 in FIG. 1;
FIG. 3 is a schematic flow chart of one embodiment of step S22 in FIG. 2;
FIG. 4 is a schematic flow chart illustrating an embodiment of step S3 in FIG. 1;
FIG. 5 is a timing diagram illustrating an embodiment of a human body detection method according to the present application;
FIG. 6 is a schematic structural diagram of an embodiment of the human body detecting device according to the present application;
fig. 7 is a schematic diagram of a framework of an embodiment of the device with a storage function according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a human body detection method according to the present application. The method comprises the following steps:
s1: and acquiring an intermediate frequency signal, wherein the intermediate frequency signal is formed by mixing a transmitting signal of the millimeter wave radar and a received echo signal.
S2: and detecting the intermediate frequency signal by using a region detection algorithm, and judging whether a target enters a designated region.
Specifically, in this embodiment, before step S2, a dynamic target detection filter is used to eliminate the radar echo of the static target in the intermediate frequency signal, so as to ensure that there is no radar echo of the static target in the intermediate frequency signal, and it is convenient to determine whether there is a dynamic target in the designated area, so as to improve the efficiency and accuracy of subsequent detection.
Specifically, please refer to fig. 2, wherein fig. 2 is a flowchart illustrating an embodiment of step S2 in fig. 1. The step S2 specifically includes:
s20: and detecting the intermediate frequency signal by using a region detection algorithm, and judging whether a dynamic target exists in the intermediate frequency signal.
Specifically, a dynamic target detection filter is used for eliminating radar echoes of static targets in the intermediate frequency signals, an area detection algorithm is used for detecting the intermediate frequency signals, and whether dynamic targets exist in the intermediate frequency signals or not is judged.
S21: and if so, obtaining the distance between the dynamic target and the millimeter wave radar.
Specifically, in this embodiment, if a dynamic target exists in the intermediate frequency signal, the distance between the dynamic target and the millimeter wave radar is obtained by using a one-dimensional fast fourier transform formula.
S22: if not, the current area detection zone bit of the designated area is obtained.
Specifically, if no dynamic target exists in the intermediate frequency signal, it indicates that no target enters or leaves the designated area. In one embodiment, please refer to fig. 3, wherein fig. 3 is a flowchart illustrating an embodiment of step S22 in fig. 2. The step S22 specifically includes:
s30: and obtaining a current region detection flag bit of the specified region.
Specifically, if no dynamic target exists in the intermediate frequency signal, that is, no target enters or leaves the designated area, a current area detection flag of the designated area is obtained, specifically, the current area detection flag is a result obtained by detecting the intermediate frequency signal by using an area detection algorithm last time, and the result may be a first flag or a second flag.
S31: and judging whether the current area detection zone bit of the current designated area is the first zone bit.
S32: if yes, detecting the intermediate frequency signal by utilizing a multi-time micro-motion detection algorithm so as to judge whether people exist in the specified area.
Specifically, if the current region detection flag of the current designated region is the first flag, the intermediate frequency signal is detected by using a multi-time inching detection algorithm to determine whether a person is in the designated region. The intermediate frequency signal is detected by a multi-time micro-motion detection algorithm, so that whether a human body exists in the designated area can be detected, and the error is reduced, so that the accuracy of the human body detection result is improved.
S33: if not, the process returns to step S1.
Specifically, if the current area detection flag bit of the current designated area is the second flag bit, it indicates that no person is in the current designated area, and the step of obtaining the intermediate frequency signal is returned to start the next detection cycle.
By the method, when no target enters or leaves the designated area, whether a dynamic target exists in the designated area or not can be judged, if yes, a multi-time micro-motion detection algorithm is executed, and if no dynamic target exists in the designated area, the step of obtaining the intermediate frequency signal is returned to, so that an invalid detection process is prevented from being executed, and the efficiency of the detection process is improved.
S23: and judging whether the distance between the dynamic target and the millimeter wave radar is smaller than or equal to a first threshold value.
Specifically, the first threshold value relates to the size and energy of the dynamic target itself, and a signal emitted by the millimeter wave radar, and the like, and is not limited in this application.
S24: and if so, judging that the dynamic target enters the specified area.
Specifically, if the distance between the dynamic target and the millimeter wave radar is less than or equal to the first threshold value, it is determined that the dynamic target enters the designated area.
In one embodiment, after detecting the intermediate frequency signal by using a region detection algorithm and determining that a target enters a designated region, a region detection flag bit of the designated region is set as a first flag bit.
S25: if not, the dynamic target is judged to leave the designated area.
Specifically, if the distance between the dynamic target and the millimeter-wave radar is greater than the first threshold value, it is determined that the dynamic target is away from the designated area.
In one embodiment, the intermediate frequency signal is detected by using a region detection algorithm, and it is determined that a target leaves a designated region, a region detection flag bit of the designated region is set as a second flag bit, and the step of acquiring the intermediate frequency signal is returned.
Specifically, in this embodiment, the first flag bit is different from the second flag bit, where the first flag bit may be represented by "1" and the second flag bit may be represented by "0". Of course, in other embodiments, the first flag bit may also be represented by "present", and the second flag bit may be represented by "absent", which only needs to be able to distinguish the first flag bit from the second flag bit, and this application does not limit this.
S3: if yes, detecting the intermediate frequency signal by utilizing a multi-time micro-motion detection algorithm so as to judge whether people exist in the specified area.
Specifically, in this embodiment, if the intermediate frequency signal is detected by using the region detection algorithm and it is determined that a target enters the designated region, the intermediate frequency signal is detected by using the multi-tap detection algorithm to determine whether a person is in the designated region.
Specifically, please refer to fig. 4, wherein fig. 4 is a flowchart illustrating an embodiment of step S3 in fig. 1. The step S3 specifically includes:
s40: and obtaining the distance information of the dynamic target, and analyzing the phase sequence information of the distance peak in the distance information.
Specifically, in the present embodiment, the distance information of the dynamic target is obtained by using a one-dimensional fast fourier transform formula, and the phase sequence information of the distance peak in the distance information is analyzed. Of course, in other embodiments, other manners may also be used to obtain the distance information of the dynamic target and resolve the phase sequence information of the distance peak in the distance information, which is not limited in this application.
S41: and performing band-pass filtering processing on the phase sequence information to extract a frequency spectrum in a target respiratory frequency range.
Specifically, a band-pass filtering process is employed to extract a frequency spectrum within a target breathing frequency range, wherein the target breathing frequency range may be set to a human breathing frequency, for example, 0.1-0.5 Hz. Of course, in other embodiments, the target breathing frequency range may be replaced by another frequency range specific to the human body, and it is only necessary to ensure that the frequency spectrum extracted in the range is the human body characteristic frequency spectrum, which is not limited in the present application.
Specifically, in the present embodiment, since the multi-tap detection algorithm is used to detect the intermediate frequency signal in step S3, a plurality of frequency spectrums within the target respiratory frequency range can be obtained.
S42: a power spectral density within a plurality of target respiratory frequency ranges is obtained from the frequency spectrum.
Specifically, the plurality of spectra extracted in step S41 are analyzed, and power spectral densities in a plurality of target respiratory frequency ranges are obtained by calculation.
S43: a mean value of the plurality of power spectral densities is obtained and it is determined whether the mean value is greater than a second threshold.
Specifically, the average value of the plurality of power spectral densities in step S42 is obtained by an average value calculation method, and is compared with the second threshold value to determine whether the average value is greater than the second threshold value. In this way, errors of results can be reduced, thereby improving the accuracy of human body detection results.
S44: and if so, judging that the specified area is occupied.
Specifically, in the present embodiment, if the average value of the plurality of power spectral densities is greater than the second threshold, it is determined that a human body exists in the designated area, that is, a human body exists.
S45: if not, judging that no person exists in the designated area.
Specifically, in this embodiment, if the average of the power spectral densities is equal to or less than the second threshold, it is determined that no human body exists in the designated area, that is, no human body exists.
S4: if no person is present, the process returns to step S1.
Specifically, if the intermediate frequency signal is detected by the multi-tap detection algorithm and it is determined that no person is present in the designated area, the process returns to step S1, i.e., the step of acquiring the intermediate frequency signal, so as to start the next detection cycle. Referring to fig. 5, fig. 5 is a timing diagram of an embodiment of the human body detection method of the present application. As shown in fig. 5, a primary region detection algorithm and a multiple micro-motion detection algorithm are used as a detection period, and after the intermediate frequency signal is detected by the region detection algorithm and a target enters a designated region, the intermediate frequency signal is detected by the multiple micro-motion detection algorithm to determine whether a human body exists in the designated region. The jog detection algorithm may be executed multiple times, for example, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, etc., which is not limited in this application. By the method, the error of the result can be reduced, the reliability of the detection result is ensured, and the accuracy of the human body detection result is improved.
By the design scheme, the two detection algorithms, namely the area detection algorithm and the micro-motion detection algorithm, are fused on a result level, and the micro-motion area detection algorithm is used for judging whether a human body exists in the designated area for many times, so that the error of the result can be reduced, and the accuracy of the human body detection result is improved.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an embodiment of a human body detection device according to the present application, the device includes a memory 200 and a processor 202 coupled to each other, program instructions are stored in the memory 200, and the processor 202 is configured to execute the program instructions to implement the human body detection method mentioned in any of the embodiments.
Specifically, the processor 202 may also be referred to as a CPU (Central Processing Unit). The processor 202 may be an integrated circuit chip having signal processing capabilities. The Processor 202 may also be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. In addition, processor 202 may be implemented collectively by a plurality of integrated circuit chips.
Referring to fig. 7, fig. 7 is a schematic diagram of a framework of an embodiment of a device with a storage function according to the present application. The device 30 stores program data 300, which can be read by a computer, and the program data 300 can be executed by a processor to implement the human body detection method mentioned in any of the above embodiments. The program data 300 may be stored in the apparatus 30 with a storage function in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. The aforementioned storage device includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
In summary, different from the situation in the prior art, in the present application, two detection algorithms, namely, an area detection algorithm and a fine motion detection algorithm, are fused on a result level, and a plurality of fine motion detection algorithms are used to determine whether a human body exists in a designated area, so that the error of the result can be reduced, and the accuracy of the detection result can be improved.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A human detection method, comprising:
acquiring an intermediate frequency signal, wherein the intermediate frequency signal is formed by mixing a transmitting signal of a millimeter wave radar and a received echo signal;
detecting the intermediate frequency signal by using a region detection algorithm and judging whether a target enters a designated region, and detecting the intermediate frequency signal by using a multi-time micro-motion detection algorithm to judge whether a person exists in the designated region;
if no, returning to the step of acquiring the intermediate frequency signal.
2. The human body detection method according to claim 1,
after the step of detecting the intermediate frequency signal by using the region detection algorithm and judging that the target enters the designated region, the method further comprises the following steps: and setting the area detection zone bit of the designated area as a first zone bit.
3. The human body detection method according to claim 2, further comprising:
responding to the detection of the intermediate frequency signal by using a region detection algorithm and judging that a target leaves a designated region, setting a region detection zone bit of the designated region as a second zone bit, and returning to the step of acquiring the intermediate frequency signal; wherein the first flag bit is different from the second flag bit.
4. The human body detection method according to claim 3, further comprising:
responding to the detection of the intermediate frequency signal by using a region detection algorithm and judging whether no target enters or leaves the designated region, and obtaining a current region detection zone bit of the designated region;
responding to the current region detection zone bit as a first zone bit, and detecting the intermediate frequency signal by utilizing a multi-time inching detection algorithm to judge whether a person exists in the specified region;
and responding to the second zone bit of the current zone detection zone bit, and returning to the step of acquiring the intermediate frequency signal.
5. The human body detection method according to claim 1, wherein the step of detecting the intermediate frequency signal in response to using a region detection algorithm and determining that a target enters a designated region comprises:
responding to the detection of the intermediate frequency signal by using a region detection algorithm and judging that a dynamic target exists in the intermediate frequency signal, and obtaining the distance between the dynamic target and the millimeter wave radar;
judging whether the distance is smaller than or equal to a first threshold value;
and if so, judging that the dynamic target enters the specified area.
6. The human body detection method according to claim 1, wherein the step of detecting the intermediate frequency signal by using a multi-tap detection algorithm to determine whether there is a human body in the designated area comprises:
obtaining distance information of the dynamic target, and analyzing phase sequence information of a distance peak value in the distance information;
performing band-pass filtering processing on the phase sequence information to extract a frequency spectrum in a target respiratory frequency range;
obtaining a power spectral density within a plurality of the target respiratory frequency ranges from the frequency spectrum;
obtaining a mean value of a plurality of the power spectral densities, and judging whether the mean value is larger than a second threshold value;
and if so, judging that the specified area is occupied.
7. The human body detection method according to claim 6, wherein the step of obtaining the distance information of the dynamic target and analyzing the phase sequence information of the distance peak in the distance information comprises:
and obtaining the distance information of the dynamic target by using a one-dimensional fast Fourier transform formula, and analyzing the phase sequence information of a distance peak value in the distance information.
8. The human body detection method according to claim 1, wherein before the step of detecting the intermediate frequency signal by using the region detection algorithm and determining that the target enters the designated region, the method further comprises:
and eliminating radar echo of a static target in the intermediate frequency signal by using a dynamic target detection filter.
9. A human body detection apparatus comprising a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the human body detection method of any one of claims 1 to 8.
10. An apparatus having a storage function, wherein program data is stored and can be read by a computer, and the program data can be executed by a processor to realize the human body detection method according to any one of claims 1 to 8.
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